This post is part of a series of "learning everything with R: An R book list". You can clink on this link to see other relevant posts.

As R is more and more popular in the industry as well as in the academics for analyzing financial data. For people unfamiliar with R, this post suggests some books for learning financial data analysis using R. From our teaching and learning R experience, the fast way to learn R is to start with the topics you have been familiar with. Thus, the book list below suits people with some background in finance but are not R user. These books below will provide useful guidance for your R learning journey. Try to read and compare these books to find what really fits you.

Fundamental theories: Time series modeling with R

Book Cover

Extracted summary

Book Title: Time Series Analysis and Its Applications
With R Examples
Author: Shumway, Robert H., Stoffer, David S.
This book presents a balanced and comprehensive
treatment of both time and frequency domain methods
with accompanying theory. Theory and methodology
are separated to allow presentations on different levels.

Book Title: Applied Time Series Analysis with R
Author: Wayne A. Woodward, Henry L. Gray, Alan C. Elliott
This book includes examples across a variety of fields,
develops theory, and provides an R-based software
package to aid in addressing time series problems
in a broad spectrum of fields.

Book Title: Practical Time Series Forecasting with R:
A Hands-On Guide
Author: Galit Shmueli and Kenneth C. Lichtendahl
This book providea an applied approach to time-series
forecasting which is an essential component of predictive
analytics. This book also introduces popular forecasting
methods and approaches used in a variety of business applications.

Book Title: Modeling Financial Time Series with S-PLUS®
Author: Eric Zivot and Jiahui Wang
This book represents an integration of theory, methods
, and examples using the S-PLUS statistical modeling
language and the S+FinMetrics module to facilitate the
practice of financial econometrics. This is the first
book to show the power of S-PLUS for the analysis of
time series data.

Book Title: Time Series Analysis With Applications in R
Author: Jonathan D.Cryer and Kung-Sik Chan
This book presents an accessible approach to understanding
time series models and their applications. The new edition
devotes two chapters to the frequency domain
and three to time series regression models, models for
heteroscedasticity, and threshold models.

Book Title: Statistics and Data Analysis for Financial Engineering
Author: David Ruppert and David S. Matteson
This book contains an ideal blend of innovative
research and practical applications, tackles
relevant investor problems, and provides a
multi-disciplined approach, solving problems
from both fundamental and non-traditional methods

Book Title: Financial Analytics with R
Author: David Ruppert and David S. Matteson
This book give examples using financial markets and
economic data to illustrate important concepts.
R Labs with real-data exercises give students practice
in data analysis.

Book Title: R in Finance and Economics
Author: Abhay Kumar Singh and David E Allen
This book provides an introduction to the statistical software
R and its application with an empirical approach in finance
and economics. It is specifically targeted towards undergraduate
and graduate students. It provides beginner-level introduction
to R using RStudio and reproducible research examples.

Book Title: An Introduction to Analysis of Financial Data with R
Author: Ruey S. Tsay
This book explores basic concepts of visualization of financial
data. Through a fundamental balance between theory and
applications, the book supplies readers with an accessible approach
to financial econometric models and their applications to
real-world empirical research.

Book Title: Statistical Analysis of Financial Data in R
Author: René Carmona
Although there are many books on mathematical finance, few deal
with the statistical aspects of modern data analysis as applied
to financial problems. This textbook fills this gap by addressing
some of the most challenging issues facing financial engineers. It
shows how modern statistical techniques can be used in
the solutions of concrete financial problems.

Book Title: Multivariate Time Series Analysis
Author: Ruey S. Tsay
This book is the much anticipated sequel coming from one of
the most influential and prominent experts on the topic of time
series. Through a fundamental balance of theory and methodology,
the book supplies readers with a comprehensible approach to
financial econometric models and their applications to real-world
empirical research.

Book Title: Computational Finance
Author: Argimiro Arratia
This book teaches you how to use the statistical tools
and methods available in the free software R, for
processing and analyzing real financial data

Book Title: Forecasting: principles and practice
Author: Rob J Hyndman and George Athana­sopou­los
This textbook provides a comprehensive introduction to
forecasting methods and presents enough information about
each method for readers to use them sensibly.

Book Title: Option Pricing and Estimation of Financial Models with R
Author: Stefano M. Iacus
This book presents inference and simulation of stochastic process
in the field of model calibration for financial times series
modelled by continuous time processes and numerical option pricing.
It also introduces the bases of probability theory and goes on to
explain how to model financial times series with continuous models.

Book Title: Quantitative Trading with R
Author: Georgakopoulos, H.
This book offers a winning strategy for devising
expertly-crafted and workable trading models using
the R open source programming language, providing
readers with a step-by-step approach to understanding
complex quantitative finance problems and building
functional computer code.

Book Title: Mastering R for Quantitative Finance
Author: Edina Berlinger et al.
This book is organized as a step-by-step practical guide to
using R. Starting with time series analysis, you will also
learn how to forecast the volume for VWAP Trading.
Among other topics, the book covers FX derivatives,
interest rate derivatives, and optimal hedging.

Book Title: Numerical Methods and Optimization in Finance
Author: Manfred Gilli et al.
This book describes computational finance tools.
It covers fundamental numerical analysis and
computational techniques, such as option pricing,
and gives special attention to simulation
and optimization.

Book Title: Tools for Computational Finance
Author: Seydel, Rüdiger
This book covers on an introductory level the very
important issue of computational aspects of
derivative pricing.

Book Title: Financial Risk Forecasting
Author: Jon Danielsson
This book is a complete introduction to practical
quantitative risk management, with a focus on market
risk. It brings together the three key disciplines
of finance, statistics and modeling (programming)

Notice that the information above is directly collected from the publisher website and we just summarize it for you. Further details about these books can be assessed by clicking the links to the book publisher. If you would like to get a quick review of financial data analysis using R, see our recent presentation here.

Finally, since more and more books are published these years to address using R in financial data analysis, the book list above might not be comprehensive. You are very welcome to leave the comments below to tell us what we missed. We will try to add them to the list ASAP!

Page last updated on 25 Nov. 2016.

R is a great tool to visualize your data: it is free to use and has lots packages to make beautiful plots. In this post, we gonna teach you how to make time plots to visualize stock returns with data from Yahoo finance. For those not familiar with how to automatically download data from Yahoo Finance with R, we suggest that you take a look at our recent presentation. Or just follow the steps bellow and copy the code and paste it to R console (RStudio is recommended).

Step 2: Loading Data from Yahoo Finance

We gonna extract US stocks prices (SP500) and Australian Stocks prices (ASX200) from Yahoo Finance with quanmod package. Notice that we further convert the daily prices into monthly with the command to.period.

Step 3: Converting prices into returns

Here, we use adjusted closing prices which include the adjustments for divident payments and stock splits to calculate total returns or holding period returns. If you want to calculate price return or capital gains, you use closing prices

Step 4: Draw the time plot!

A little more

We can add grey shaded areas to the plot to mark the important period like the period of global financial crisis(set from 2007-01-31 to 2008-12-31 in this tutorial). This is done by using the arguments, period.areas and period.color.
[code]chart.TimeSeries(cbind(Return.M.SP500,Return.M.ASX200),legend.loc="bottomleft",date.format ="%b-%Y",las = 2, ylab = "Returns",main = "Time Series Plot",period.areas = c("2007-01-31::2008-12-31"),period.color = c("gray"))[/code]

退休金制度牽繫著每位勞動者的退休生活品質，最早退休金制度可追溯到1889年德國建立起年金保險體系，再到後來隨收隨付制(Pay as you go)，此皆由政府負擔，視為國家福利的一部分，此時退休基金是完全的公有化。這種由國家稅收來照顧年長國民的制度，係以世代間所得分配的觀點來進行，年輕世代將部分所得經由政府轉給退休者，等到年輕一代退休再由下一代接棒投入工作所得。這樣的制度要能夠永續進行必須至少要幾個前提，第一個是勞動者平均薪資須大於或等於通貨膨脹，再者勞動人口要能維持或持續增長讓扶養比維持在適當比率，政府對於退休基金的提撥比率要足夠。但自1981年以來，由於人口老化和政府的稅收不足以支撐這個體系，很多國家已逐漸轉向退休金私有化(Pension privatization)制度，也就是由勞動者和雇者主要負擔未來退休生活，政府則轉為輔助者的角色。

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About the founder of this blog

I am a professional economist and R programmer who helps academics and practitioners in the industry implement their theories into practice. My expertise is in the areas of statistical programming (using R and Bayesian statistics software, BUGS), financial analysis (pension simulation and portfolio maximization), simulation methods (bootstrapping and Monte Carol), school fina﻿​nce (equity and adequacy), productivity measurement (using DEA and SFA), structural changes (based on scale and scope economies), and data visualization.
More info about me: http://liangchengzhang.weebly.com/